Overview

Dataset statistics

Number of variables24
Number of observations45466
Missing cells105562
Missing cells (%)9.7%
Duplicate rows16
Duplicate rows (%)< 0.1%
Total size in memory8.3 MiB
Average record size in memory192.0 B

Variable types

Categorical2
Text17
Numeric4
Boolean1

Alerts

Dataset has 16 (< 0.1%) duplicate rowsDuplicates
adult is highly imbalanced (99.8%)Imbalance
status is highly imbalanced (97.0%)Imbalance
video is highly imbalanced (97.9%)Imbalance
belongs_to_collection has 40972 (90.1%) missing valuesMissing
homepage has 37684 (82.9%) missing valuesMissing
overview has 954 (2.1%) missing valuesMissing
tagline has 25054 (55.1%) missing valuesMissing
revenue has 38052 (83.7%) zerosZeros
runtime has 1558 (3.4%) zerosZeros
vote_average has 2998 (6.6%) zerosZeros
vote_count has 2899 (6.4%) zerosZeros

Reproduction

Analysis started2024-04-25 21:16:15.087809
Analysis finished2024-04-25 21:16:21.149542
Duration6.06 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

adult
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
False
45454 
True
 
9
- Written by Ørnås
 
1
Rune Balot goes to a casino connected to the October corporation to try to wrap up her case once and for all.
 
1
Avalanche Sharks tells the story of a bikini contest that turns into a horrifying affair when it is hit by a shark avalanche.
 
1

Length

Max length126
Median length5
Mean length5.0050807
Min length4

Characters and Unicode

Total characters227561
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowFalse
2nd rowFalse
3rd rowFalse
4th rowFalse
5th rowFalse

Common Values

ValueCountFrequency (%)
False 45454
> 99.9%
True 9
 
< 0.1%
- Written by Ørnås 1
 
< 0.1%
Rune Balot goes to a casino connected to the October corporation to try to wrap up her case once and for all. 1
 
< 0.1%
Avalanche Sharks tells the story of a bikini contest that turns into a horrifying affair when it is hit by a shark avalanche. 1
 
< 0.1%

Length

2024-04-25T17:16:21.181228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-25T17:16:21.222673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
false 45454
99.9%
true 9
 
< 0.1%
to 4
 
< 0.1%
a 4
 
< 0.1%
the 2
 
< 0.1%
avalanche 2
 
< 0.1%
by 2
 
< 0.1%
when 1
 
< 0.1%
contest 1
 
< 0.1%
hit 1
 
< 0.1%
Other values (32) 32
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 45479
20.0%
a 45475
20.0%
s 45465
20.0%
l 45461
20.0%
F 45454
20.0%
49
 
< 0.1%
r 25
 
< 0.1%
t 23
 
< 0.1%
o 19
 
< 0.1%
n 17
 
< 0.1%
Other values (24) 94
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 227561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 45479
20.0%
a 45475
20.0%
s 45465
20.0%
l 45461
20.0%
F 45454
20.0%
49
 
< 0.1%
r 25
 
< 0.1%
t 23
 
< 0.1%
o 19
 
< 0.1%
n 17
 
< 0.1%
Other values (24) 94
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 227561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 45479
20.0%
a 45475
20.0%
s 45465
20.0%
l 45461
20.0%
F 45454
20.0%
49
 
< 0.1%
r 25
 
< 0.1%
t 23
 
< 0.1%
o 19
 
< 0.1%
n 17
 
< 0.1%
Other values (24) 94
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 227561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 45479
20.0%
a 45475
20.0%
s 45465
20.0%
l 45461
20.0%
F 45454
20.0%
49
 
< 0.1%
r 25
 
< 0.1%
t 23
 
< 0.1%
o 19
 
< 0.1%
n 17
 
< 0.1%
Other values (24) 94
 
< 0.1%

belongs_to_collection
Text

MISSING 

Distinct1698
Distinct (%)37.8%
Missing40972
Missing (%)90.1%
Memory size355.3 KiB
2024-04-25T17:16:21.294351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length184
Median length167
Mean length141.40632
Min length8

Characters and Unicode

Total characters635480
Distinct characters170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique393 ?
Unique (%)8.7%

Sample

1st row{'id': 10194, 'name': 'Toy Story Collection', 'poster_path': '/7G9915LfUQ2lVfwMEEhDsn3kT4B.jpg', 'backdrop_path': '/9FBwqcd9IRruEDUrTdcaafOMKUq.jpg'}
2nd row{'id': 119050, 'name': 'Grumpy Old Men Collection', 'poster_path': '/nLvUdqgPgm3F85NMCii9gVFUcet.jpg', 'backdrop_path': '/hypTnLot2z8wpFS7qwsQHW1uV8u.jpg'}
3rd row{'id': 96871, 'name': 'Father of the Bride Collection', 'poster_path': '/nts4iOmNnq7GNicycMJ9pSAn204.jpg', 'backdrop_path': '/7qwE57OVZmMJChBpLEbJEmzUydk.jpg'}
4th row{'id': 645, 'name': 'James Bond Collection', 'poster_path': '/HORpg5CSkmeQlAolx3bKMrKgfi.jpg', 'backdrop_path': '/6VcVl48kNKvdXOZfJPdarlUGOsk.jpg'}
5th row{'id': 117693, 'name': 'Balto Collection', 'poster_path': '/w0ZgH6Lgxt2bQYnf1ss74UvYftm.jpg', 'backdrop_path': '/9VM5LiJV0bGb1st1KyHA3cVnO2G.jpg'}
ValueCountFrequency (%)
name 4497
 
9.7%
id 4491
 
9.7%
backdrop_path 4491
 
9.7%
poster_path 4491
 
9.7%
collection 3746
 
8.1%
none 1771
 
3.8%
the 1146
 
2.5%
of 230
 
0.5%
series 147
 
0.3%
139
 
0.3%
Other values (6634) 21083
45.6%
2024-04-25T17:16:21.434976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 59225
 
9.3%
41739
 
6.6%
p 29081
 
4.6%
a 25710
 
4.0%
o 25040
 
3.9%
e 24229
 
3.8%
t 23203
 
3.7%
: 18063
 
2.8%
n 16731
 
2.6%
r 15825
 
2.5%
Other values (160) 356634
56.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 635480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 59225
 
9.3%
41739
 
6.6%
p 29081
 
4.6%
a 25710
 
4.0%
o 25040
 
3.9%
e 24229
 
3.8%
t 23203
 
3.7%
: 18063
 
2.8%
n 16731
 
2.6%
r 15825
 
2.5%
Other values (160) 356634
56.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 635480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 59225
 
9.3%
41739
 
6.6%
p 29081
 
4.6%
a 25710
 
4.0%
o 25040
 
3.9%
e 24229
 
3.8%
t 23203
 
3.7%
: 18063
 
2.8%
n 16731
 
2.6%
r 15825
 
2.5%
Other values (160) 356634
56.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 635480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 59225
 
9.3%
41739
 
6.6%
p 29081
 
4.6%
a 25710
 
4.0%
o 25040
 
3.9%
e 24229
 
3.8%
t 23203
 
3.7%
: 18063
 
2.8%
n 16731
 
2.6%
r 15825
 
2.5%
Other values (160) 356634
56.1%

budget
Text

Distinct1226
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
2024-04-25T17:16:21.565814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length1
Mean length2.2153917
Min length1

Characters and Unicode

Total characters100725
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique839 ?
Unique (%)1.8%

Sample

1st row30000000
2nd row65000000
3rd row0
4th row16000000
5th row0
ValueCountFrequency (%)
0 36573
80.4%
5000000 286
 
0.6%
10000000 259
 
0.6%
20000000 243
 
0.5%
2000000 242
 
0.5%
15000000 226
 
0.5%
3000000 223
 
0.5%
25000000 206
 
0.5%
1000000 197
 
0.4%
30000000 190
 
0.4%
Other values (1216) 6821
 
15.0%
2024-04-25T17:16:21.762024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 84525
83.9%
1 3222
 
3.2%
5 3201
 
3.2%
2 2555
 
2.5%
3 1792
 
1.8%
4 1325
 
1.3%
6 1147
 
1.1%
7 1119
 
1.1%
8 1102
 
1.1%
9 660
 
0.7%
Other values (39) 77
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 84525
83.9%
1 3222
 
3.2%
5 3201
 
3.2%
2 2555
 
2.5%
3 1792
 
1.8%
4 1325
 
1.3%
6 1147
 
1.1%
7 1119
 
1.1%
8 1102
 
1.1%
9 660
 
0.7%
Other values (39) 77
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 84525
83.9%
1 3222
 
3.2%
5 3201
 
3.2%
2 2555
 
2.5%
3 1792
 
1.8%
4 1325
 
1.3%
6 1147
 
1.1%
7 1119
 
1.1%
8 1102
 
1.1%
9 660
 
0.7%
Other values (39) 77
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 84525
83.9%
1 3222
 
3.2%
5 3201
 
3.2%
2 2555
 
2.5%
3 1792
 
1.8%
4 1325
 
1.3%
6 1147
 
1.1%
7 1119
 
1.1%
8 1102
 
1.1%
9 660
 
0.7%
Other values (39) 77
 
0.1%

genres
Text

Distinct4069
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
2024-04-25T17:16:21.874759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length264
Median length225
Mean length62.822131
Min length2

Characters and Unicode

Total characters2856271
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2365 ?
Unique (%)5.2%

Sample

1st row[{'id': 16, 'name': 'Animation'}, {'id': 35, 'name': 'Comedy'}, {'id': 10751, 'name': 'Family'}]
2nd row[{'id': 12, 'name': 'Adventure'}, {'id': 14, 'name': 'Fantasy'}, {'id': 10751, 'name': 'Family'}]
3rd row[{'id': 10749, 'name': 'Romance'}, {'id': 35, 'name': 'Comedy'}]
4th row[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}]
5th row[{'id': 35, 'name': 'Comedy'}]
ValueCountFrequency (%)
id 91106
24.6%
name 91106
24.6%
drama 20265
 
5.5%
18 20265
 
5.5%
35 13182
 
3.6%
comedy 13182
 
3.6%
53 7624
 
2.1%
thriller 7624
 
2.1%
romance 6735
 
1.8%
10749 6735
 
1.8%
Other values (71) 92873
25.1%
2024-04-25T17:16:22.032559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 546636
19.1%
325231
 
11.4%
: 182212
 
6.4%
a 152966
 
5.4%
e 146936
 
5.1%
m 144238
 
5.0%
, 139188
 
4.9%
i 130819
 
4.6%
n 126822
 
4.4%
d 107792
 
3.8%
Other values (46) 853431
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2856271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 546636
19.1%
325231
 
11.4%
: 182212
 
6.4%
a 152966
 
5.4%
e 146936
 
5.1%
m 144238
 
5.0%
, 139188
 
4.9%
i 130819
 
4.6%
n 126822
 
4.4%
d 107792
 
3.8%
Other values (46) 853431
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2856271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 546636
19.1%
325231
 
11.4%
: 182212
 
6.4%
a 152966
 
5.4%
e 146936
 
5.1%
m 144238
 
5.0%
, 139188
 
4.9%
i 130819
 
4.6%
n 126822
 
4.4%
d 107792
 
3.8%
Other values (46) 853431
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2856271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 546636
19.1%
325231
 
11.4%
: 182212
 
6.4%
a 152966
 
5.4%
e 146936
 
5.1%
m 144238
 
5.0%
, 139188
 
4.9%
i 130819
 
4.6%
n 126822
 
4.4%
d 107792
 
3.8%
Other values (46) 853431
29.9%

homepage
Text

MISSING 

Distinct7673
Distinct (%)98.6%
Missing37684
Missing (%)82.9%
Memory size355.3 KiB
2024-04-25T17:16:22.169352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length242
Median length110
Mean length36.712799
Min length13

Characters and Unicode

Total characters285699
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7610 ?
Unique (%)97.8%

Sample

1st rowhttp://toystory.disney.com/toy-story
2nd rowhttp://www.mgm.com/view/movie/757/Goldeneye/
3rd rowhttp://www.mgm.com/title_title.do?title_star=LEAVINGL
4th rowhttp://www.sevenmovie.com/
5th rowhttp://www.mgm.com/#/our-titles/2083/The-Usual-Suspects
ValueCountFrequency (%)
http://www.georgecarlin.com 12
 
0.2%
iso_3166_1 7
 
0.1%
name 7
 
0.1%
http://www.wernerherzog.com/films-by.html 7
 
0.1%
http://breakblade.jp 6
 
0.1%
http://www.kungfupanda.com 6
 
0.1%
http://www.transformersmovie.com 5
 
0.1%
http://www.missionimpossible.com 5
 
0.1%
http://www.crownintlpictures.com/tztitles.html 4
 
0.1%
http://www.jeffdunham.com 4
 
0.1%
Other values (7658) 7753
99.2%
2024-04-25T17:16:22.360956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 25849
 
9.0%
/ 25820
 
9.0%
w 19516
 
6.8%
o 18783
 
6.6%
e 18709
 
6.5%
. 15387
 
5.4%
m 15101
 
5.3%
h 13863
 
4.9%
i 13654
 
4.8%
c 11414
 
4.0%
Other values (81) 107603
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 285699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 25849
 
9.0%
/ 25820
 
9.0%
w 19516
 
6.8%
o 18783
 
6.6%
e 18709
 
6.5%
. 15387
 
5.4%
m 15101
 
5.3%
h 13863
 
4.9%
i 13654
 
4.8%
c 11414
 
4.0%
Other values (81) 107603
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 285699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 25849
 
9.0%
/ 25820
 
9.0%
w 19516
 
6.8%
o 18783
 
6.6%
e 18709
 
6.5%
. 15387
 
5.4%
m 15101
 
5.3%
h 13863
 
4.9%
i 13654
 
4.8%
c 11414
 
4.0%
Other values (81) 107603
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 285699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 25849
 
9.0%
/ 25820
 
9.0%
w 19516
 
6.8%
o 18783
 
6.6%
e 18709
 
6.5%
. 15387
 
5.4%
m 15101
 
5.3%
h 13863
 
4.9%
i 13654
 
4.8%
c 11414
 
4.0%
Other values (81) 107603
37.7%

id
Text

Distinct45436
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
2024-04-25T17:16:22.541358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.2514846
Min length1

Characters and Unicode

Total characters238764
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45407 ?
Unique (%)99.9%

Sample

1st row862
2nd row8844
3rd row15602
4th row31357
5th row11862
ValueCountFrequency (%)
141971 3
 
< 0.1%
12600 2
 
< 0.1%
109962 2
 
< 0.1%
69234 2
 
< 0.1%
5511 2
 
< 0.1%
159849 2
 
< 0.1%
25541 2
 
< 0.1%
42495 2
 
< 0.1%
298721 2
 
< 0.1%
14788 2
 
< 0.1%
Other values (45426) 45445
> 99.9%
2024-04-25T17:16:22.770292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%
Distinct45417
Distinct (%)99.9%
Missing17
Missing (%)< 0.1%
Memory size355.3 KiB
2024-04-25T17:16:22.948890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9994719
Min length1

Characters and Unicode

Total characters409017
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45387 ?
Unique (%)99.9%

Sample

1st rowtt0114709
2nd rowtt0113497
3rd rowtt0113228
4th rowtt0114885
5th rowtt0113041
ValueCountFrequency (%)
tt1180333 3
 
< 0.1%
0 3
 
< 0.1%
tt0295682 2
 
< 0.1%
tt0100361 2
 
< 0.1%
tt1821641 2
 
< 0.1%
tt0062229 2
 
< 0.1%
tt0173769 2
 
< 0.1%
tt1327820 2
 
< 0.1%
tt0022879 2
 
< 0.1%
tt0111613 2
 
< 0.1%
Other values (45407) 45427
> 99.9%
2024-04-25T17:16:23.240154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 409017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 409017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 409017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%
Distinct92
Distinct (%)0.2%
Missing11
Missing (%)< 0.1%
Memory size355.3 KiB
2024-04-25T17:16:23.332918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.000154
Min length2

Characters and Unicode

Total characters90917
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en 32269
71.0%
fr 2438
 
5.4%
it 1529
 
3.4%
ja 1350
 
3.0%
de 1080
 
2.4%
es 994
 
2.2%
ru 826
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 409
 
0.9%
Other values (82) 3608
 
7.9%
2024-04-25T17:16:23.453832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%
Distinct43373
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
2024-04-25T17:16:23.627585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length109
Median length84
Mean length16.323494
Min length1

Characters and Unicode

Total characters742164
Distinct characters2946
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41712 ?
Unique (%)91.7%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II
ValueCountFrequency (%)
the 10261
 
7.8%
of 3309
 
2.5%
a 1674
 
1.3%
in 1275
 
1.0%
and 1072
 
0.8%
la 1007
 
0.8%
863
 
0.7%
to 806
 
0.6%
de 702
 
0.5%
man 509
 
0.4%
Other values (35324) 110301
83.7%
2024-04-25T17:16:23.874243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 742164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 742164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 742164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

overview
Text

MISSING 

Distinct44307
Distinct (%)99.5%
Missing954
Missing (%)2.1%
Memory size355.3 KiB
2024-04-25T17:16:24.069123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1000
Median length785
Mean length323.32155
Min length1

Characters and Unicode

Total characters14391689
Distinct characters429
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44247 ?
Unique (%)99.4%

Sample

1st rowLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.
2nd rowWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.
3rd rowA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.
4th rowCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.
5th rowJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.
ValueCountFrequency (%)
the 138357
 
5.6%
a 99037
 
4.0%
and 75407
 
3.1%
to 73442
 
3.0%
of 69723
 
2.8%
in 48228
 
2.0%
is 36550
 
1.5%
his 36210
 
1.5%
with 23933
 
1.0%
her 21518
 
0.9%
Other values (97181) 1830623
74.6%
2024-04-25T17:16:24.318414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2410599
16.7%
e 1366183
 
9.5%
a 942278
 
6.5%
t 936476
 
6.5%
i 853105
 
5.9%
o 831419
 
5.8%
n 824147
 
5.7%
s 769188
 
5.3%
r 745638
 
5.2%
h 601821
 
4.2%
Other values (419) 4110835
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14391689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2410599
16.7%
e 1366183
 
9.5%
a 942278
 
6.5%
t 936476
 
6.5%
i 853105
 
5.9%
o 831419
 
5.8%
n 824147
 
5.7%
s 769188
 
5.3%
r 745638
 
5.2%
h 601821
 
4.2%
Other values (419) 4110835
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14391689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2410599
16.7%
e 1366183
 
9.5%
a 942278
 
6.5%
t 936476
 
6.5%
i 853105
 
5.9%
o 831419
 
5.8%
n 824147
 
5.7%
s 769188
 
5.3%
r 745638
 
5.2%
h 601821
 
4.2%
Other values (419) 4110835
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14391689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2410599
16.7%
e 1366183
 
9.5%
a 942278
 
6.5%
t 936476
 
6.5%
i 853105
 
5.9%
o 831419
 
5.8%
n 824147
 
5.7%
s 769188
 
5.3%
r 745638
 
5.2%
h 601821
 
4.2%
Other values (419) 4110835
28.6%
Distinct43771
Distinct (%)96.3%
Missing5
Missing (%)< 0.1%
Memory size355.3 KiB
2024-04-25T17:16:24.493785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length21
Median length8
Mean length7.943908
Min length3

Characters and Unicode

Total characters361138
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42988 ?
Unique (%)94.6%

Sample

1st row21.946943
2nd row17.015539
3rd row11.7129
4th row3.859495
5th row8.387519
ValueCountFrequency (%)
0.0 66
 
0.1%
0.000308 43
 
0.1%
0.00022 40
 
0.1%
0.000844 38
 
0.1%
0.001177 38
 
0.1%
0.000578 38
 
0.1%
1e-06 30
 
0.1%
0.002001 28
 
0.1%
0.000001 26
 
0.1%
0.003013 21
 
< 0.1%
Other values (43764) 45096
99.2%
2024-04-25T17:16:24.721746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 49276
13.6%
. 45411
12.6%
1 40034
11.1%
2 31499
8.7%
3 29504
8.2%
4 28645
7.9%
5 28466
7.9%
7 27647
7.7%
6 27612
7.6%
8 26649
7.4%
Other values (15) 26395
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 361138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 49276
13.6%
. 45411
12.6%
1 40034
11.1%
2 31499
8.7%
3 29504
8.2%
4 28645
7.9%
5 28466
7.9%
7 27647
7.7%
6 27612
7.6%
8 26649
7.4%
Other values (15) 26395
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 361138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 49276
13.6%
. 45411
12.6%
1 40034
11.1%
2 31499
8.7%
3 29504
8.2%
4 28645
7.9%
5 28466
7.9%
7 27647
7.7%
6 27612
7.6%
8 26649
7.4%
Other values (15) 26395
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 361138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 49276
13.6%
. 45411
12.6%
1 40034
11.1%
2 31499
8.7%
3 29504
8.2%
4 28645
7.9%
5 28466
7.9%
7 27647
7.7%
6 27612
7.6%
8 26649
7.4%
Other values (15) 26395
7.3%
Distinct45024
Distinct (%)99.9%
Missing386
Missing (%)0.8%
Memory size355.3 KiB
2024-04-25T17:16:24.900857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length32
Mean length31.971628
Min length12

Characters and Unicode

Total characters1441281
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44977 ?
Unique (%)99.8%

Sample

1st row/rhIRbceoE9lR4veEXuwCC2wARtG.jpg
2nd row/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg
3rd row/6ksm1sjKMFLbO7UY2i6G1ju9SML.jpg
4th row/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg
5th row/e64sOI48hQXyru7naBFyssKFxVd.jpg
ValueCountFrequency (%)
5d7ubsegdyone6lql6xs7s6olcw.jpg 5
 
< 0.1%
qw1oqlohizrhxzqrpkimyr0oxzn.jpg 4
 
< 0.1%
2kslzxoaw0hmnguvpcnqlcdxfr9.jpg 4
 
< 0.1%
8vsz9coczxocw2we2qene1h1fko.jpg 3
 
< 0.1%
cdwvc18urfedqjjxqjyrmogdc0h.jpg 3
 
< 0.1%
bql0pvhbq8jmw3njcl38kw0coem.jpg 2
 
< 0.1%
sgmpdg6je1zki0tix9b4pp6yn02.jpg 2
 
< 0.1%
glfxjfsihypipksg86vqxo029om.jpg 2
 
< 0.1%
nfkokpudnnijrrf0mtfvoigzhyc.jpg 2
 
< 0.1%
rgmtc9atzsnwssl5vnlagvx1pi6.jpg 2
 
< 0.1%
Other values (45020) 45057
99.9%
2024-04-25T17:16:25.116584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 65293
 
4.5%
p 65148
 
4.5%
j 65043
 
4.5%
/ 45077
 
3.1%
. 45077
 
3.1%
v 20444
 
1.4%
d 20329
 
1.4%
m 20322
 
1.4%
q 20256
 
1.4%
t 20248
 
1.4%
Other values (56) 1054044
73.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1441281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
g 65293
 
4.5%
p 65148
 
4.5%
j 65043
 
4.5%
/ 45077
 
3.1%
. 45077
 
3.1%
v 20444
 
1.4%
d 20329
 
1.4%
m 20322
 
1.4%
q 20256
 
1.4%
t 20248
 
1.4%
Other values (56) 1054044
73.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1441281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
g 65293
 
4.5%
p 65148
 
4.5%
j 65043
 
4.5%
/ 45077
 
3.1%
. 45077
 
3.1%
v 20444
 
1.4%
d 20329
 
1.4%
m 20322
 
1.4%
q 20256
 
1.4%
t 20248
 
1.4%
Other values (56) 1054044
73.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1441281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
g 65293
 
4.5%
p 65148
 
4.5%
j 65043
 
4.5%
/ 45077
 
3.1%
. 45077
 
3.1%
v 20444
 
1.4%
d 20329
 
1.4%
m 20322
 
1.4%
q 20256
 
1.4%
t 20248
 
1.4%
Other values (56) 1054044
73.1%
Distinct22708
Distinct (%)49.9%
Missing3
Missing (%)< 0.1%
Memory size355.3 KiB
2024-04-25T17:16:25.292371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1252
Median length954
Mean length70.098828
Min length2

Characters and Unicode

Total characters3186903
Distinct characters293
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20344 ?
Unique (%)44.7%

Sample

1st row[{'name': 'Pixar Animation Studios', 'id': 3}]
2nd row[{'name': 'TriStar Pictures', 'id': 559}, {'name': 'Teitler Film', 'id': 2550}, {'name': 'Interscope Communications', 'id': 10201}]
3rd row[{'name': 'Warner Bros.', 'id': 6194}, {'name': 'Lancaster Gate', 'id': 19464}]
4th row[{'name': 'Twentieth Century Fox Film Corporation', 'id': 306}]
5th row[{'name': 'Sandollar Productions', 'id': 5842}, {'name': 'Touchstone Pictures', 'id': 9195}]
ValueCountFrequency (%)
id 70546
 
17.6%
name 70546
 
17.6%
12719
 
3.2%
films 9457
 
2.4%
pictures 9267
 
2.3%
productions 9061
 
2.3%
film 6679
 
1.7%
entertainment 5156
 
1.3%
corporation 2190
 
0.5%
company 1769
 
0.4%
Other values (42195) 203834
50.8%
2024-04-25T17:16:25.525202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 422867
 
13.3%
355774
 
11.2%
i 177505
 
5.6%
e 165212
 
5.2%
n 160535
 
5.0%
a 147709
 
4.6%
: 141099
 
4.4%
m 114830
 
3.6%
, 107909
 
3.4%
d 104017
 
3.3%
Other values (283) 1289446
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3186903
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 422867
 
13.3%
355774
 
11.2%
i 177505
 
5.6%
e 165212
 
5.2%
n 160535
 
5.0%
a 147709
 
4.6%
: 141099
 
4.4%
m 114830
 
3.6%
, 107909
 
3.4%
d 104017
 
3.3%
Other values (283) 1289446
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3186903
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 422867
 
13.3%
355774
 
11.2%
i 177505
 
5.6%
e 165212
 
5.2%
n 160535
 
5.0%
a 147709
 
4.6%
: 141099
 
4.4%
m 114830
 
3.6%
, 107909
 
3.4%
d 104017
 
3.3%
Other values (283) 1289446
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3186903
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 422867
 
13.3%
355774
 
11.2%
i 177505
 
5.6%
e 165212
 
5.2%
n 160535
 
5.0%
a 147709
 
4.6%
: 141099
 
4.4%
m 114830
 
3.6%
, 107909
 
3.4%
d 104017
 
3.3%
Other values (283) 1289446
40.5%
Distinct2393
Distinct (%)5.3%
Missing3
Missing (%)< 0.1%
Memory size355.3 KiB
2024-04-25T17:16:25.709789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1039
Median length649
Mean length53.200493
Min length2

Characters and Unicode

Total characters2418654
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1768 ?
Unique (%)3.9%

Sample

1st row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
2nd row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
3rd row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
4th row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
5th row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
ValueCountFrequency (%)
iso_3166_1 49423
18.1%
name 49423
18.1%
united 25275
9.2%
states 21154
7.7%
of 21153
7.7%
america 21153
7.7%
us 21153
7.7%
6282
 
2.3%
gb 4094
 
1.5%
kingdom 4094
 
1.5%
Other values (344) 50140
18.3%
2024-04-25T17:16:25.952821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 395379
16.3%
227881
 
9.4%
e 130095
 
5.4%
a 119929
 
5.0%
i 107991
 
4.5%
6 98847
 
4.1%
_ 98846
 
4.1%
1 98846
 
4.1%
: 98846
 
4.1%
n 96933
 
4.0%
Other values (59) 945061
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2418654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 395379
16.3%
227881
 
9.4%
e 130095
 
5.4%
a 119929
 
5.0%
i 107991
 
4.5%
6 98847
 
4.1%
_ 98846
 
4.1%
1 98846
 
4.1%
: 98846
 
4.1%
n 96933
 
4.0%
Other values (59) 945061
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2418654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 395379
16.3%
227881
 
9.4%
e 130095
 
5.4%
a 119929
 
5.0%
i 107991
 
4.5%
6 98847
 
4.1%
_ 98846
 
4.1%
1 98846
 
4.1%
: 98846
 
4.1%
n 96933
 
4.0%
Other values (59) 945061
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2418654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 395379
16.3%
227881
 
9.4%
e 130095
 
5.4%
a 119929
 
5.0%
i 107991
 
4.5%
6 98847
 
4.1%
_ 98846
 
4.1%
1 98846
 
4.1%
: 98846
 
4.1%
n 96933
 
4.0%
Other values (59) 945061
39.1%
Distinct17336
Distinct (%)38.2%
Missing87
Missing (%)0.2%
Memory size355.3 KiB
2024-04-25T17:16:26.116141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9994491
Min length1

Characters and Unicode

Total characters453765
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8573 ?
Unique (%)18.9%

Sample

1st row1995-10-30
2nd row1995-12-15
3rd row1995-12-22
4th row1995-12-22
5th row1995-02-10
ValueCountFrequency (%)
2008-01-01 136
 
0.3%
2009-01-01 121
 
0.3%
2007-01-01 118
 
0.3%
2005-01-01 111
 
0.2%
2006-01-01 101
 
0.2%
2002-01-01 96
 
0.2%
2004-01-01 90
 
0.2%
2001-01-01 84
 
0.2%
2003-01-01 76
 
0.2%
1997-01-01 69
 
0.2%
Other values (17326) 44377
97.8%
2024-04-25T17:16:26.320373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 97600
21.5%
- 90752
20.0%
1 84056
18.5%
2 52806
11.6%
9 39773
8.8%
3 15435
 
3.4%
8 15279
 
3.4%
6 15021
 
3.3%
5 14836
 
3.3%
7 14289
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 453765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 97600
21.5%
- 90752
20.0%
1 84056
18.5%
2 52806
11.6%
9 39773
8.8%
3 15435
 
3.4%
8 15279
 
3.4%
6 15021
 
3.3%
5 14836
 
3.3%
7 14289
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 453765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 97600
21.5%
- 90752
20.0%
1 84056
18.5%
2 52806
11.6%
9 39773
8.8%
3 15435
 
3.4%
8 15279
 
3.4%
6 15021
 
3.3%
5 14836
 
3.3%
7 14289
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 453765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 97600
21.5%
- 90752
20.0%
1 84056
18.5%
2 52806
11.6%
9 39773
8.8%
3 15435
 
3.4%
8 15279
 
3.4%
6 15021
 
3.3%
5 14836
 
3.3%
7 14289
 
3.1%

revenue
Real number (ℝ)

ZEROS 

Distinct6863
Distinct (%)15.1%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11209349
Minimum0
Maximum2.7879651 × 109
Zeros38052
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2024-04-25T17:16:26.397770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile47808918
Maximum2.7879651 × 109
Range2.7879651 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64332247
Coefficient of variation (CV)5.7391602
Kurtosis237.51059
Mean11209349
Median Absolute Deviation (MAD)0
Skewness12.265983
Sum5.0957698 × 1011
Variance4.138638 × 1015
MonotonicityNot monotonic
2024-04-25T17:16:26.447504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38052
83.7%
12000000 20
 
< 0.1%
11000000 19
 
< 0.1%
10000000 19
 
< 0.1%
2000000 18
 
< 0.1%
6000000 17
 
< 0.1%
5000000 14
 
< 0.1%
8000000 13
 
< 0.1%
500000 13
 
< 0.1%
1 12
 
< 0.1%
Other values (6853) 7263
 
16.0%
ValueCountFrequency (%)
0 38052
83.7%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 9
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2787965087 1
< 0.1%
2068223624 1
< 0.1%
1845034188 1
< 0.1%
1519557910 1
< 0.1%
1513528810 1
< 0.1%
1506249360 1
< 0.1%
1405403694 1
< 0.1%
1342000000 1
< 0.1%
1274219009 1
< 0.1%
1262886337 1
< 0.1%

runtime
Real number (ℝ)

ZEROS 

Distinct353
Distinct (%)0.8%
Missing263
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean94.128199
Minimum0
Maximum1256
Zeros1558
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2024-04-25T17:16:26.497910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q185
median95
Q3107
95-th percentile138
Maximum1256
Range1256
Interquartile range (IQR)22

Descriptive statistics

Standard deviation38.40781
Coefficient of variation (CV)0.40803724
Kurtosis93.217158
Mean94.128199
Median Absolute Deviation (MAD)11
Skewness4.4659579
Sum4254877
Variance1475.1599
MonotonicityNot monotonic
2024-04-25T17:16:26.549359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2556
 
5.6%
0 1558
 
3.4%
100 1470
 
3.2%
95 1412
 
3.1%
93 1214
 
2.7%
96 1104
 
2.4%
92 1080
 
2.4%
94 1062
 
2.3%
91 1057
 
2.3%
88 1032
 
2.3%
Other values (343) 31658
69.6%
ValueCountFrequency (%)
0 1558
3.4%
1 107
 
0.2%
2 33
 
0.1%
3 48
 
0.1%
4 51
 
0.1%
5 51
 
0.1%
6 72
 
0.2%
7 103
 
0.2%
8 78
 
0.2%
9 63
 
0.1%
ValueCountFrequency (%)
1256 1
< 0.1%
1140 2
< 0.1%
931 1
< 0.1%
925 1
< 0.1%
900 1
< 0.1%
877 1
< 0.1%
874 1
< 0.1%
840 2
< 0.1%
780 1
< 0.1%
720 1
< 0.1%
Distinct1931
Distinct (%)4.2%
Missing6
Missing (%)< 0.1%
Memory size355.3 KiB
2024-04-25T17:16:26.693101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length765
Median length40
Mean length46.928289
Min length2

Characters and Unicode

Total characters2133360
Distinct characters184
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1366 ?
Unique (%)3.0%

Sample

1st row[{'iso_639_1': 'en', 'name': 'English'}]
2nd row[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'fr', 'name': 'Français'}]
3rd row[{'iso_639_1': 'en', 'name': 'English'}]
4th row[{'iso_639_1': 'en', 'name': 'English'}]
5th row[{'iso_639_1': 'en', 'name': 'English'}]
ValueCountFrequency (%)
iso_639_1 53300
24.4%
name 53300
24.4%
english 28745
13.2%
en 28745
13.2%
4809
 
2.2%
fr 4196
 
1.9%
français 4196
 
1.9%
deutsch 2625
 
1.2%
de 2625
 
1.2%
español 2413
 
1.1%
Other values (203) 33488
15.3%
2024-04-25T17:16:26.926892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 426400
20.0%
172982
 
8.1%
n 120605
 
5.7%
_ 106600
 
5.0%
: 106600
 
5.0%
s 99222
 
4.7%
i 94120
 
4.4%
e 92748
 
4.3%
a 75235
 
3.5%
, 64969
 
3.0%
Other values (174) 773879
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2133360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 426400
20.0%
172982
 
8.1%
n 120605
 
5.7%
_ 106600
 
5.0%
: 106600
 
5.0%
s 99222
 
4.7%
i 94120
 
4.4%
e 92748
 
4.3%
a 75235
 
3.5%
, 64969
 
3.0%
Other values (174) 773879
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2133360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 426400
20.0%
172982
 
8.1%
n 120605
 
5.7%
_ 106600
 
5.0%
: 106600
 
5.0%
s 99222
 
4.7%
i 94120
 
4.4%
e 92748
 
4.3%
a 75235
 
3.5%
, 64969
 
3.0%
Other values (174) 773879
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2133360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 426400
20.0%
172982
 
8.1%
n 120605
 
5.7%
_ 106600
 
5.0%
: 106600
 
5.0%
s 99222
 
4.7%
i 94120
 
4.4%
e 92748
 
4.3%
a 75235
 
3.5%
, 64969
 
3.0%
Other values (174) 773879
36.3%

status
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing87
Missing (%)0.2%
Memory size355.3 KiB
Released
45014 
Rumored
 
230
Post Production
 
98
In Production
 
20
Planned
 
15

Length

Max length15
Median length8
Mean length8.0119218
Min length7

Characters and Unicode

Total characters363573
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 45014
99.0%
Rumored 230
 
0.5%
Post Production 98
 
0.2%
In Production 20
 
< 0.1%
Planned 15
 
< 0.1%
Canceled 2
 
< 0.1%
(Missing) 87
 
0.2%

Length

2024-04-25T17:16:26.998420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-25T17:16:27.108712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
released 45014
98.9%
rumored 230
 
0.5%
production 118
 
0.3%
post 98
 
0.2%
in 20
 
< 0.1%
planned 15
 
< 0.1%
canceled 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 363573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 363573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 363573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

tagline
Text

MISSING 

Distinct20283
Distinct (%)99.4%
Missing25054
Missing (%)55.1%
Memory size355.3 KiB
2024-04-25T17:16:27.256642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length297
Median length204
Mean length47.002841
Min length1

Characters and Unicode

Total characters959422
Distinct characters170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20177 ?
Unique (%)98.8%

Sample

1st rowRoll the dice and unleash the excitement!
2nd rowStill Yelling. Still Fighting. Still Ready for Love.
3rd rowFriends are the people who let you be yourself... and never let you forget it.
4th rowJust When His World Is Back To Normal... He's In For The Surprise Of His Life!
5th rowA Los Angeles Crime Saga
ValueCountFrequency (%)
the 11004
 
6.3%
a 6820
 
3.9%
of 4406
 
2.5%
to 3586
 
2.1%
is 2800
 
1.6%
in 2693
 
1.5%
and 2686
 
1.5%
you 2389
 
1.4%
1585
 
0.9%
for 1524
 
0.9%
Other values (15108) 134566
77.3%
2024-04-25T17:16:27.497046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153795
16.0%
e 94486
 
9.8%
t 57309
 
6.0%
o 56611
 
5.9%
a 51521
 
5.4%
n 47539
 
5.0%
i 46086
 
4.8%
r 45029
 
4.7%
s 42399
 
4.4%
h 37192
 
3.9%
Other values (160) 327455
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 959422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
153795
16.0%
e 94486
 
9.8%
t 57309
 
6.0%
o 56611
 
5.9%
a 51521
 
5.4%
n 47539
 
5.0%
i 46086
 
4.8%
r 45029
 
4.7%
s 42399
 
4.4%
h 37192
 
3.9%
Other values (160) 327455
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 959422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
153795
16.0%
e 94486
 
9.8%
t 57309
 
6.0%
o 56611
 
5.9%
a 51521
 
5.4%
n 47539
 
5.0%
i 46086
 
4.8%
r 45029
 
4.7%
s 42399
 
4.4%
h 37192
 
3.9%
Other values (160) 327455
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 959422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
153795
16.0%
e 94486
 
9.8%
t 57309
 
6.0%
o 56611
 
5.9%
a 51521
 
5.4%
n 47539
 
5.0%
i 46086
 
4.8%
r 45029
 
4.7%
s 42399
 
4.4%
h 37192
 
3.9%
Other values (160) 327455
34.1%

title
Text

Distinct42277
Distinct (%)93.0%
Missing6
Missing (%)< 0.1%
Memory size355.3 KiB
2024-04-25T17:16:27.683140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length105
Median length79
Mean length16.708535
Min length1

Characters and Unicode

Total characters759570
Distinct characters287
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39947 ?
Unique (%)87.9%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II
ValueCountFrequency (%)
the 14571
 
10.7%
of 4938
 
3.6%
a 2244
 
1.6%
in 1697
 
1.2%
and 1634
 
1.2%
to 1055
 
0.8%
763
 
0.6%
man 665
 
0.5%
love 664
 
0.5%
for 602
 
0.4%
Other values (24431) 107634
78.9%
2024-04-25T17:16:27.923568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 759570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 759570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 759570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

video
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size355.3 KiB
False
45367 
True
 
93
(Missing)
 
6
ValueCountFrequency (%)
False 45367
99.8%
True 93
 
0.2%
(Missing) 6
 
< 0.1%
2024-04-25T17:16:27.990837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

vote_average
Real number (ℝ)

ZEROS 

Distinct92
Distinct (%)0.2%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.6182072
Minimum0
Maximum10
Zeros2998
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2024-04-25T17:16:28.032498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36.8
95-th percentile7.8
Maximum10
Range10
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.924216
Coefficient of variation (CV)0.34249644
Kurtosis2.5004022
Mean5.6182072
Median Absolute Deviation (MAD)0.9
Skewness-1.5189901
Sum255403.7
Variance3.7026072
MonotonicityNot monotonic
2024-04-25T17:16:28.083846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2998
 
6.6%
6 2468
 
5.4%
5 2001
 
4.4%
7 1886
 
4.1%
6.5 1722
 
3.8%
6.3 1603
 
3.5%
5.5 1381
 
3.0%
5.8 1369
 
3.0%
6.4 1350
 
3.0%
6.7 1342
 
3.0%
Other values (82) 27340
60.1%
ValueCountFrequency (%)
0 2998
6.6%
0.5 13
 
< 0.1%
0.7 1
 
< 0.1%
1 105
 
0.2%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.3 13
 
< 0.1%
1.4 5
 
< 0.1%
1.5 30
 
0.1%
1.6 6
 
< 0.1%
ValueCountFrequency (%)
10 190
0.4%
9.8 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 18
 
< 0.1%
9.4 3
 
< 0.1%
9.3 18
 
< 0.1%
9.2 4
 
< 0.1%
9.1 3
 
< 0.1%
9 159
0.3%
8.9 7
 
< 0.1%

vote_count
Real number (ℝ)

ZEROS 

Distinct1820
Distinct (%)4.0%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean109.89734
Minimum0
Maximum14075
Zeros2899
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2024-04-25T17:16:28.130583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q334
95-th percentile434
Maximum14075
Range14075
Interquartile range (IQR)31

Descriptive statistics

Standard deviation491.31037
Coefficient of variation (CV)4.4706303
Kurtosis151.2028
Mean109.89734
Median Absolute Deviation (MAD)8
Skewness10.450232
Sum4995933
Variance241385.88
MonotonicityNot monotonic
2024-04-25T17:16:28.181561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3264
 
7.2%
2 3132
 
6.9%
0 2899
 
6.4%
3 2787
 
6.1%
4 2480
 
5.5%
5 2097
 
4.6%
6 1747
 
3.8%
7 1570
 
3.5%
8 1359
 
3.0%
9 1194
 
2.6%
Other values (1810) 22931
50.4%
ValueCountFrequency (%)
0 2899
6.4%
1 3264
7.2%
2 3132
6.9%
3 2787
6.1%
4 2480
5.5%
5 2097
4.6%
6 1747
3.8%
7 1570
3.5%
8 1359
3.0%
9 1194
 
2.6%
ValueCountFrequency (%)
14075 1
< 0.1%
12269 1
< 0.1%
12114 1
< 0.1%
12000 1
< 0.1%
11444 1
< 0.1%
11187 1
< 0.1%
10297 1
< 0.1%
10014 1
< 0.1%
9678 1
< 0.1%
9634 1
< 0.1%

Interactions

2024-04-25T17:16:20.298474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:19.764503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:19.950356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:20.142538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:20.390923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:19.809375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:19.992490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:20.181500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:20.435411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:19.853752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:20.061716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:20.220155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:20.476087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:19.901908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:20.099909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:16:20.258724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-25T17:16:20.580815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-25T17:16:20.760320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

adultbelongs_to_collectionbudgetgenreshomepageidimdb_idoriginal_languageoriginal_titleoverviewpopularityposter_pathproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevideovote_averagevote_count
0False{'id': 10194, 'name': 'Toy Story Collection', 'poster_path': '/7G9915LfUQ2lVfwMEEhDsn3kT4B.jpg', 'backdrop_path': '/9FBwqcd9IRruEDUrTdcaafOMKUq.jpg'}30000000[{'id': 16, 'name': 'Animation'}, {'id': 35, 'name': 'Comedy'}, {'id': 10751, 'name': 'Family'}]http://toystory.disney.com/toy-story862tt0114709enToy StoryLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.21.946943/rhIRbceoE9lR4veEXuwCC2wARtG.jpg[{'name': 'Pixar Animation Studios', 'id': 3}][{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-10-30373554033.081.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedNoneToy StoryFalse7.75415.0
1FalseNone65000000[{'id': 12, 'name': 'Adventure'}, {'id': 14, 'name': 'Fantasy'}, {'id': 10751, 'name': 'Family'}]None8844tt0113497enJumanjiWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.17.015539/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg[{'name': 'TriStar Pictures', 'id': 559}, {'name': 'Teitler Film', 'id': 2550}, {'name': 'Interscope Communications', 'id': 10201}][{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-15262797249.0104.0[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'fr', 'name': 'Français'}]ReleasedRoll the dice and unleash the excitement!JumanjiFalse6.92413.0
2False{'id': 119050, 'name': 'Grumpy Old Men Collection', 'poster_path': '/nLvUdqgPgm3F85NMCii9gVFUcet.jpg', 'backdrop_path': '/hypTnLot2z8wpFS7qwsQHW1uV8u.jpg'}0[{'id': 10749, 'name': 'Romance'}, {'id': 35, 'name': 'Comedy'}]None15602tt0113228enGrumpier Old MenA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.11.7129/6ksm1sjKMFLbO7UY2i6G1ju9SML.jpg[{'name': 'Warner Bros.', 'id': 6194}, {'name': 'Lancaster Gate', 'id': 19464}][{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-220.0101.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedStill Yelling. Still Fighting. Still Ready for Love.Grumpier Old MenFalse6.592.0
3FalseNone16000000[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}]None31357tt0114885enWaiting to ExhaleCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.3.859495/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg[{'name': 'Twentieth Century Fox Film Corporation', 'id': 306}][{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-2281452156.0127.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedFriends are the people who let you be yourself... and never let you forget it.Waiting to ExhaleFalse6.134.0
4False{'id': 96871, 'name': 'Father of the Bride Collection', 'poster_path': '/nts4iOmNnq7GNicycMJ9pSAn204.jpg', 'backdrop_path': '/7qwE57OVZmMJChBpLEbJEmzUydk.jpg'}0[{'id': 35, 'name': 'Comedy'}]None11862tt0113041enFather of the Bride Part IIJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.8.387519/e64sOI48hQXyru7naBFyssKFxVd.jpg[{'name': 'Sandollar Productions', 'id': 5842}, {'name': 'Touchstone Pictures', 'id': 9195}][{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-02-1076578911.0106.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedJust When His World Is Back To Normal... He's In For The Surprise Of His Life!Father of the Bride Part IIFalse5.7173.0
5FalseNone60000000[{'id': 28, 'name': 'Action'}, {'id': 80, 'name': 'Crime'}, {'id': 18, 'name': 'Drama'}, {'id': 53, 'name': 'Thriller'}]None949tt0113277enHeatObsessive master thief, Neil McCauley leads a top-notch crew on various insane heists throughout Los Angeles while a mentally unstable detective, Vincent Hanna pursues him without rest. Each man recognizes and respects the ability and the dedication of the other even though they are aware their cat-and-mouse game may end in violence.17.924927/zMyfPUelumio3tiDKPffaUpsQTD.jpg[{'name': 'Regency Enterprises', 'id': 508}, {'name': 'Forward Pass', 'id': 675}, {'name': 'Warner Bros.', 'id': 6194}][{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-15187436818.0170.0[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'es', 'name': 'Español'}]ReleasedA Los Angeles Crime SagaHeatFalse7.71886.0
6FalseNone58000000[{'id': 35, 'name': 'Comedy'}, {'id': 10749, 'name': 'Romance'}]None11860tt0114319enSabrinaAn ugly duckling having undergone a remarkable change, still harbors feelings for her crush: a carefree playboy, but not before his business-focused brother has something to say about it.6.677277/jQh15y5YB7bWz1NtffNZmRw0s9D.jpg[{'name': 'Paramount Pictures', 'id': 4}, {'name': 'Scott Rudin Productions', 'id': 258}, {'name': 'Mirage Enterprises', 'id': 932}, {'name': 'Sandollar Productions', 'id': 5842}, {'name': 'Constellation Entertainment', 'id': 14941}, {'name': 'Worldwide', 'id': 55873}, {'name': 'Mont Blanc Entertainment GmbH', 'id': 58079}][{'iso_3166_1': 'DE', 'name': 'Germany'}, {'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-150.0127.0[{'iso_639_1': 'fr', 'name': 'Français'}, {'iso_639_1': 'en', 'name': 'English'}]ReleasedYou are cordially invited to the most surprising merger of the year.SabrinaFalse6.2141.0
7FalseNone0[{'id': 28, 'name': 'Action'}, {'id': 12, 'name': 'Adventure'}, {'id': 18, 'name': 'Drama'}, {'id': 10751, 'name': 'Family'}]None45325tt0112302enTom and HuckA mischievous young boy, Tom Sawyer, witnesses a murder by the deadly Injun Joe. Tom becomes friends with Huckleberry Finn, a boy with no future and no family. Tom has to choose between honoring a friendship or honoring an oath because the town alcoholic is accused of the murder. Tom and Huck go through several adventures trying to retrieve evidence.2.561161/sGO5Qa55p7wTu7FJcX4H4xIVKvS.jpg[{'name': 'Walt Disney Pictures', 'id': 2}][{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-220.097.0[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'de', 'name': 'Deutsch'}]ReleasedThe Original Bad Boys.Tom and HuckFalse5.445.0
8FalseNone0[{'id': 35, 'name': 'Comedy'}]None47686tt0119019enDream with the FishesTerry is a suicidal voyeur who treats a dying addict to a final binge, but Terry will only do this if he promises to kill him.0.684192/tlUqk3T9KOSinF4SL0LqXtOyFm4.jpg[][{'iso_3166_1': 'US', 'name': 'United States of America'}]1997-01-010.097.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedAn oddball odyssey about voyeurism, LSD and nude bowling!Dream with the FishesFalse7.710.0
9FalseNone35000000[{'id': 28, 'name': 'Action'}, {'id': 12, 'name': 'Adventure'}, {'id': 53, 'name': 'Thriller'}]None9091tt0114576enSudden DeathInternational action superstar Jean Claude Van Damme teams with Powers Boothe in a Tension-packed, suspense thriller, set against the back-drop of a Stanley Cup game.Van Damme portrays a father whose daughter is suddenly taken during a championship hockey game. With the captors demanding a billion dollars by game's end, Van Damme frantically sets a plan in motion to rescue his daughter and abort an impending explosion before the final buzzer...5.23158/eoWvKD60lT95Ss1MYNgVExpo5iU.jpg[{'name': 'Universal Pictures', 'id': 33}, {'name': 'Imperial Entertainment', 'id': 21437}, {'name': 'Signature Entertainment', 'id': 23770}][{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-12-2264350171.0106.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedTerror goes into overtime.Sudden DeathFalse5.5174.0
adultbelongs_to_collectionbudgetgenreshomepageidimdb_idoriginal_languageoriginal_titleoverviewpopularityposter_pathproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevideovote_averagevote_count
45456FalseNone0[{'id': 27, 'name': 'Horror'}, {'id': 9648, 'name': 'Mystery'}, {'id': 53, 'name': 'Thriller'}]None84419tt0038621enHouse of HorrorsAn unsuccessful sculptor saves a madman named "The Creeper" from drowning. Seeing an opportunity for revenge, he tricks the psycho into murdering his critics.0.222814/yMnq9mL5uYxbRgwKqyz1cVGCJYJ.jpg[{'name': 'Universal Pictures', 'id': 33}][{'iso_3166_1': 'US', 'name': 'United States of America'}]1946-03-290.065.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedMeet...The CREEPER!House of HorrorsFalse6.38.0
45457FalseNone0[{'id': 9648, 'name': 'Mystery'}, {'id': 27, 'name': 'Horror'}]None390959tt0265736enShadow of the Blair WitchIn this true-crime documentary, we delve into the murder spree that was the inspiration for Joe Berlinger's "Book of Shadows: Blair Witch 2".0.076061/q75tCM4pFmUzdCg0gqcOQquCaYf.jpg[][]2000-10-220.045.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedNoneShadow of the Blair WitchFalse7.02.0
45458FalseNone0[{'id': 27, 'name': 'Horror'}]None289923tt0252966enThe Burkittsville 7A film archivist revisits the story of Rustin Parr, a hermit thought to have murdered seven children while under the possession of the Blair Witch.0.38645/lXtoHVdej6kS1Dc7KAhw05sMos9.jpg[{'name': 'Neptune Salad Entertainment', 'id': 27570}, {'name': 'Pirie Productions', 'id': 27571}][{'iso_3166_1': 'US', 'name': 'United States of America'}]2000-10-030.030.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedDo you know what happened 50 years before "The Blair Witch Project"?The Burkittsville 7False7.01.0
45459FalseNone0[{'id': 878, 'name': 'Science Fiction'}]None222848tt0112613enCaged Heat 3000It's the year 3000 AD. The world's most dangerous women are banished to a remote asteroid 45 million light years from earth. Kira Murphy doesn't belong; wrongfully accused of a crime she did not commit, she's thrown in this interplanetary prison and left to her own defenses. But Kira's a fighter, and soon she finds herself in the middle of a female gang war; where everyone wants a piece of the action... and a piece of her! "Caged Heat 3000" takes the Women-in-Prison genre to a whole new level... and a whole new galaxy!0.661558/4lF9LH0b0Z1X94xGK9IOzqEW6k1.jpg[{'name': 'Concorde-New Horizons', 'id': 4688}][{'iso_3166_1': 'US', 'name': 'United States of America'}]1995-01-010.085.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedNoneCaged Heat 3000False3.51.0
45460FalseNone0[{'id': 18, 'name': 'Drama'}, {'id': 28, 'name': 'Action'}, {'id': 10749, 'name': 'Romance'}]None30840tt0102797enRobin HoodYet another version of the classic epic, with enough variation to make it interesting. The story is the same, but some of the characters are quite different from the usual, in particular Uma Thurman's very special maid Marian. The photography is also great, giving the story a somewhat darker tone.5.683753/fQC46NglNiEMZBv5XHoyLuOWoN5.jpg[{'name': 'Westdeutscher Rundfunk (WDR)', 'id': 7025}, {'name': 'Working Title Films', 'id': 10163}, {'name': '20th Century Fox Television', 'id': 16323}, {'name': 'CanWest Global Communications', 'id': 38978}][{'iso_3166_1': 'CA', 'name': 'Canada'}, {'iso_3166_1': 'DE', 'name': 'Germany'}, {'iso_3166_1': 'GB', 'name': 'United Kingdom'}, {'iso_3166_1': 'US', 'name': 'United States of America'}]1991-05-130.0104.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedNoneRobin HoodFalse5.726.0
45461FalseNone0[{'id': 18, 'name': 'Drama'}, {'id': 10751, 'name': 'Family'}]http://www.imdb.com/title/tt6209470/439050tt6209470faرگ خوابRising and falling between a man and woman.0.072051/jldsYflnId4tTWPx8es3uzsB1I8.jpg[][{'iso_3166_1': 'IR', 'name': 'Iran'}]None0.090.0[{'iso_639_1': 'fa', 'name': 'فارسی'}]ReleasedRising and falling between a man and womanSubdueFalse4.01.0
45462FalseNone0[{'id': 18, 'name': 'Drama'}]None111109tt2028550tlSiglo ng PagluluwalAn artist struggles to finish his work while a storyline about a cult plays in his head.0.178241/xZkmxsNmYXJbKVsTRLLx3pqGHx7.jpg[{'name': 'Sine Olivia', 'id': 19653}][{'iso_3166_1': 'PH', 'name': 'Philippines'}]2011-11-170.0360.0[{'iso_639_1': 'tl', 'name': ''}]ReleasedNoneCentury of BirthingFalse9.03.0
45463FalseNone0[{'id': 28, 'name': 'Action'}, {'id': 18, 'name': 'Drama'}, {'id': 53, 'name': 'Thriller'}]None67758tt0303758enBetrayalWhen one of her hits goes wrong, a professional assassin ends up with a suitcase full of a million dollars belonging to a mob boss ...0.903007/d5bX92nDsISNhu3ZT69uHwmfCGw.jpg[{'name': 'American World Pictures', 'id': 6165}][{'iso_3166_1': 'US', 'name': 'United States of America'}]2003-08-010.090.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedA deadly game of wits.BetrayalFalse3.86.0
45464FalseNone0[]None227506tt0008536enSatana likuyushchiyIn a small town live two brothers, one a minister and the other one a hunchback painter of the chapel who lives with his wife. One dreadful and stormy night, a stranger knocks at the door asking for shelter. The stranger talks about all the good things of the earthly life the minister is missing because of his puritanical faith. The minister comes to accept the stranger's viewpoint but it is others who will pay the consequences because the minister will discover the human pleasures thanks to, ehem, his sister- in -law… The tormented minister and his cuckolded brother will die in a strange accident in the chapel and later an infant will be born from the minister's adulterous relationship.0.003503/aorBPO7ak8e8iJKT5OcqYxU3jlK.jpg[{'name': 'Yermoliev', 'id': 88753}][{'iso_3166_1': 'RU', 'name': 'Russia'}]1917-10-210.087.0[]ReleasedNoneSatan TriumphantFalse0.00.0
45465FalseNone0[]None461257tt6980792enQueerama50 years after decriminalisation of homosexuality in the UK, director Daisy Asquith mines the jewels of the BFI archive to take us into the relationships, desires, fears and expressions of gay men and women in the 20th century.0.163015/s5UkZt6NTsrS7ZF0Rh8nzupRlIU.jpg[][{'iso_3166_1': 'GB', 'name': 'United Kingdom'}]2017-06-090.075.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedNoneQueeramaFalse0.00.0

Duplicate rows

Most frequently occurring

adultbelongs_to_collectionbudgetgenreshomepageidimdb_idoriginal_languageoriginal_titleoverviewpopularityposter_pathproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevideovote_averagevote_count# duplicates
13FalseNaN0[{'id': 53, 'name': 'Thriller'}, {'id': 9648, 'name': 'Mystery'}]NaN141971tt1180333fiBlackoutRecovering from a nail gun shot to the head and 13 months of coma, doctor Pekka Valinta starts to unravel the mystery of his past, still suffering from total amnesia.0.411949/8VSZ9coCzxOCW2wE2Qene1H1fKO.jpg[{'name': 'Filmiteollisuus Fine', 'id': 5166}][{'iso_3166_1': 'FI', 'name': 'Finland'}]2008-12-260.0108.0[{'iso_639_1': 'fi', 'name': 'suomi'}]ReleasedWhich one is the first to return - memory or the murderer?BlackoutFalse6.73.03
0False{'id': 158365, 'name': 'Why We Fight', 'poster_path': '/fFYBLu2Hnx27CWLOMd425ExDkgK.jpg', 'backdrop_path': None}0[{'id': 99, 'name': 'Documentary'}]NaN159849tt0173769enWhy We Fight: Divide and ConquerThe third film of Frank Capra's 'Why We Fight" propaganda film series, dealing with the Nazi conquest of Western Europe in 1940.0.473322/g21ruZZ3BZeUDuKMb82kejjtufk.jpg[][{'iso_3166_1': 'US', 'name': 'United States of America'}]1943-01-010.057.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedNaNWhy We Fight: Divide and ConquerFalse5.01.02
1FalseNaN0[{'id': 12, 'name': 'Adventure'}, {'id': 16, 'name': 'Animation'}, {'id': 18, 'name': 'Drama'}, {'id': 28, 'name': 'Action'}, {'id': 10769, 'name': 'Foreign'}]NaN23305tt0295682enThe WarriorIn feudal India, a warrior (Khan) who renounces his role as the longtime enforcer to a local lord becomes the prey in a murderous hunt through the Himalayan mountains.1.967992/9GlrmbZO7VGyqhaSR1utinRJz3L.jpg[{'name': 'Filmfour', 'id': 6705}][{'iso_3166_1': 'FR', 'name': 'France'}, {'iso_3166_1': 'DE', 'name': 'Germany'}, {'iso_3166_1': 'IN', 'name': 'India'}, {'iso_3166_1': 'GB', 'name': 'United Kingdom'}]2001-09-230.086.0[{'iso_639_1': 'hi', 'name': 'हिन्दी'}]ReleasedNaNThe WarriorFalse6.315.02
2FalseNaN0[{'id': 14, 'name': 'Fantasy'}, {'id': 18, 'name': 'Drama'}, {'id': 878, 'name': 'Science Fiction'}]NaN119916tt0080000enThe TempestProspero, the true Duke of Milan is now living on an enchanted island with his daughter Miranda, the savage Caliban and Ariel, a spirit of the air. Raising a sorm to bring his brother - the usurper of his dukedom - along with his royal entourage. to the island. Prospero contrives his revenge.0.000018/gLVRTxaLtUDkfscFKPyYrCtRnTk.jpg[][]1980-02-270.0123.0[]ReleasedNaNThe TempestFalse0.00.02
3FalseNaN0[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}]NaN105045tt0111613deDas VersprechenEast-Berlin, 1961, shortly after the erection of the Wall. Konrad, Sophie and three of their friends plan a daring escape to Western Germany. The attempt is successful, except for Konrad, who remains behind. From then on, and for the next 28 years, Konrad and Sophie will attempt to meet again, in spite of the Iron Curtain. Konrad, who has become a reputed Astrophysicist, tries to take advantage of scientific congresses outside Eastern Germany to arrange encounters with Sophie. But in a country where the political police, the Stasi, monitors the moves of all suspicious people (such as Konrad's sister Barbara and her husband Harald), preserving one's privacy, ideals and self-respect becomes an exhausting fight, even as the Eastern block begins its long process of disintegration.0.122178/5WFIrBhOOgc0jGmoLxMZwWqCctO.jpg[{'name': 'Studio Babelsberg', 'id': 264}, {'name': 'Centre National de la Cinématographie', 'id': 310}, {'name': 'Odessa Films', 'id': 1712}, {'name': 'Canal+', 'id': 5358}, {'name': 'Bioskop Film', 'id': 5982}, {'name': 'Westdeutscher Rundfunk (WDR)', 'id': 7025}][{'iso_3166_1': 'DE', 'name': 'Germany'}]1995-02-160.0115.0[{'iso_639_1': 'de', 'name': 'Deutsch'}]ReleasedA love, a hope, a wall.The PromiseFalse5.01.02
4FalseNaN0[{'id': 18, 'name': 'Drama'}, {'id': 10769, 'name': 'Foreign'}]NaN42495tt0067306enKing LearKing Lear, old and tired, divides his kingdom among his daughters, giving great importance to their protestations of love for him. When Cordelia, youngest and most honest, refuses to idly flatter the old man in return for favor, he banishes her and turns for support to his remaining daughters. But Goneril and Regan have no love for him and instead plot to take all his power from him. In a parallel, Lear's loyal courtier Gloucester favors his illegitimate son Edmund after being told lies about his faithful son Edgar. Madness and tragedy befall both ill-starred fathers.0.187901/xuE1IlUCohbxMY0fiqKTT6d013n.jpg[{'name': 'Royal Shakespeare Company', 'id': 5845}, {'name': 'Laterna Film', 'id': 24737}, {'name': 'Athena Film A/S', 'id': 51549}][{'iso_3166_1': 'DK', 'name': 'Denmark'}, {'iso_3166_1': 'GB', 'name': 'United Kingdom'}]1971-02-040.0137.0[{'iso_639_1': 'en', 'name': 'English'}]RumoredNaNKing LearFalse8.03.02
5FalseNaN0[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name': 'Comedy'}]NaN168538tt0084387enNanaIn Zola's Paris, an ingenue arrives at a tony bordello: she's Nana, guileless, but quickly learning to use her erotic innocence to get what she wants. She's an actress for a soft-core filmmaker and soon is the most popular courtesan in Paris, parlaying this into a house, bought for her by a wealthy banker. She tosses him and takes up with her neighbor, a count of impeccable rectitude, and with the count's impressionable son. The count is soon fetching sticks like a dog and mortgaging his lands to satisfy her whims.1.276602/pg4PUHRFrgNfACHSh5MITQ2gYch.jpg[{'name': 'Cannon Group', 'id': 1444}, {'name': 'Metro-Goldwyn-Mayer (MGM)', 'id': 8411}][]1983-06-130.092.0[]ReleasedNaNNana, the True Key of PleasureFalse4.73.02
6FalseNaN0[{'id': 18, 'name': 'Drama'}, {'id': 878, 'name': 'Science Fiction'}, {'id': 16, 'name': 'Animation'}]NaN152795tt1821641enThe CongressMore than two decades after catapulting to stardom with The Princess Bride, an aging actress (Robin Wright, playing a version of herself) decides to take her final job: preserving her digital likeness for a future Hollywood. Through a deal brokered by her loyal, longtime agent and the head of Miramount Studios, her alias will be controlled by the studio, and will star in any film they want with no restrictions. In return, she receives healthy compensation so she can care for her ailing son and her digitized character will stay forever young. Twenty years later, under the creative vision of the studio’s head animator, Wright’s digital double rises to immortal stardom. With her contract expiring, she is invited to take part in “The Congress” convention as she makes her comeback straight into the world of future fantasy cinema.8.534039/nnKX3ahYoT7P3au92dNgLf4pKwA.jpg[{'name': 'Pandora Filmproduktion', 'id': 254}, {'name': 'Entre Chien et Loup', 'id': 3984}, {'name': 'Opus Film', 'id': 6477}, {'name': 'Bridgit Folman Film Gang', 'id': 17931}, {'name': 'Paul Thiltges Distributions', 'id': 19437}, {'name': 'Liverpool', 'id': 60111}][{'iso_3166_1': 'BE', 'name': 'Belgium'}, {'iso_3166_1': 'FR', 'name': 'France'}, {'iso_3166_1': 'DE', 'name': 'Germany'}, {'iso_3166_1': 'IL', 'name': 'Israel'}, {'iso_3166_1': 'LU', 'name': 'Luxembourg'}, {'iso_3166_1': 'PL', 'name': 'Poland'}]2013-05-16455815.0122.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedNaNThe CongressFalse6.4165.02
7FalseNaN0[{'id': 18, 'name': 'Drama'}]NaN25541tt1327820daBroderskabFormer Danish servicemen Lars and Jimmy are thrown together while training in a neo-Nazi group. Moving from hostility through grudging admiration to friendship and finally passion, events take a darker turn when their illicit relationship is uncovered.2.587911/q19Q5BRZpMXoNCA4OYodVozfjUh.jpg[][{'iso_3166_1': 'SE', 'name': 'Sweden'}, {'iso_3166_1': 'DK', 'name': 'Denmark'}]2009-10-210.090.0[{'iso_639_1': 'da', 'name': 'Dansk'}]ReleasedNaNBrotherhoodFalse7.121.02
8FalseNaN0[{'id': 28, 'name': 'Action'}, {'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}, {'id': 12, 'name': 'Adventure'}]NaN99080tt0022537enThe VikingOriginally called White Thunder, American producer Varick Frissell's 1931 film was inspired by his love for the Canadian Arctic Circle. Set in a beautifully black-and-white filmed Newfoundland, it is the story of a rivalry between two seal hunters that plays out on the ice floes during a hunt. Unsatisfied with the first cut, Frissell arranged for the crew to accompany an actual Newfoundland seal hunt on The SS Viking, on which an explosion of dynamite (carried regularly at the time on Arctic ships to combat ice jams) killed many members of the crew, including Frissell. The film was renamed in honor of the dead.0.002362/qenjwRvW9itR5pVp4CBkYfhVAOp.jpg[][]1931-06-210.070.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedActually produced during the Great Newfoundland Seal Hunt and You see the REAL thingThe VikingFalse0.00.02